Papers with Code 아카이브
Image Generation
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/apple/ml-cvpr2019-swd
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
https://github.com/apple2373/PyTorch-SmallGAN
Image Generation From Small Datasets via Batch Statistics Adaptation
https://github.com/arahosu/musicslots
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/arcelien/hawc-deep-learning
https://github.com/archinetai/bitcodes-pytorch
https://github.com/ardapekis/pixel-rnn
https://github.com/ariangc/breinchallenge
https://github.com/ariel415el/ALAE
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/ariel415el/Simple-SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ariel415el/SimplePytorch-ALAE
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/arihaz/ImplicitNeuralRepresentations
Implicit Neural Representations with Periodic Activation Functions
https://github.com/armaank/archlectures
https://github.com/arnab39/cycleGAN-PyTorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/arnavdodiedo/dog2sketch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/arnavdodiedo/sketch2face
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/arnavkj1995/face_inpainting
https://github.com/arnonbruno/ganify
https://github.com/arobey1/mbrdl
https://github.com/arpit2412/Generative-Adversarial-Network-/tree/master/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/arpitbansal297/cold-diffusion-models
https://github.com/arraiyopensource/torchgeometry
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/ars-ashuha/real-nvp-pytorch
https://github.com/artcg/BEGAN
https://github.com/artem-gorodetskii/Face-Encoder-Decoder
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/artem-gorodetskii/wikiart-latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/arturml/pytorch-wgan-gp
https://github.com/aryan-mann/style-transfer
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/asahi417/DeepDomainAdaptation
https://github.com/asarigun/MixerGANsformer
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
https://github.com/asarigun/TransGAN
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
https://github.com/asbjrnmunk/mdd-unet
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
https://github.com/ashantanu/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ashawkey/RAD-NeRF
Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition
https://github.com/ashawkey/stable-dreamfusion
https://github.com/ashen-sensored/sd_webui_SAG
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.